Skip to main content

Advertisement

Log in

Does Everyone’s Motivational Beliefs about Physical Science Decline in Secondary School?: Heterogeneity of Adolescents’ Achievement Motivation Trajectories in Physics and Chemistry

  • Empirical Research
  • Published:
Journal of Youth and Adolescence Aims and scope Submit manuscript

Abstract

Students’ motivational beliefs about learning physical science are critical for achieving positive educational outcomes. In this study, we incorporated expectancy-value theory to capture the heterogeneity of adolescents’ motivational trajectories in physics and chemistry from seventh to twelfth grade and linked these trajectories to science-related outcomes. We used a cross-sequential design based on three different cohorts of adolescents (N = 699; 51.5 % female; 95 % European American; M ages for youngest, middle, and oldest cohorts at the first wave = 13.2, 14.1, and 15.3 years) coming from ten public secondary schools. Although many studies claim that physical science motivation declines on average over time, we identified seven differential motivational trajectories of ability self-concept and task values, and found associations of these trajectories with science achievement, advanced science course taking, and science career aspirations. Adolescents’ ability self-concept and task values in physics and chemistry were also positively related and interlinked over time. Examining how students’ motivational beliefs about physical science develop in secondary school offers insight into the capacity of different groups of students to successfully adapt to their changing educational environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317–332.

    Article  Google Scholar 

  • Archambault, I., Eccles, J. S., & Vida, M. N. (2010). Ability self-concepts and subjective value in literacy: Joint trajectories from grades 1 through 12. Journal of Educational Psychology, 102, 804–816. doi:10.1037/a0021075.

    Article  Google Scholar 

  • Archambault, I., Janosz, M., Morizot, J., & Pagani, L. (2009). Adolescent behavioral, affective, and cognitive engagement in school: Relationship to dropout. Journal of School Health, 79, 408–415. doi:10.1111/j.1746-1561.2009.00428.x.

    Article  PubMed  Google Scholar 

  • Asparouhov, T., & Muthén, B. (2013). Auxiliary variables in mixture modeling: A 3-step approach using MPlus. MPlus Web Notes: No. 14.

  • Assor, A., & Connell, J. P. (1992). The validity of students’ self-reports as measures of performance affecting self-appraisals. In D. H. Schunk, & J. L. Meece (Eds.), Student perceptions in the classroom (pp. 25–47). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48, 5–37. doi:10.1016/j.jsp.2009.10.001.

    Article  PubMed  Google Scholar 

  • Bennett, J., & Hogarth, S. (2009). Would you want to talk to a scientist at a party? High school students’ attitudes to school science and to science. International Journal of Science Education, 31, 1975–1998. doi:10.1080/09500690802425581.

    Article  Google Scholar 

  • Denissen, J. J., Zarrett, N. R., & Eccles, J. S. (2007). I like to do it, I’m able, and I know I am: Longitudinal couplings between domain‐specific achievement, self‐concept, and interest. Child Development, 78, 430–447. doi:10.1111/j.1467-8624.2007.01007.x.

    Article  PubMed  Google Scholar 

  • Diallo, T. M., Morin, A. J., & Lu, H. (2016a). Impact of misspecifications of the latent variance–covariance and residual matrices on the class enumeration accuracy of growth mixture models. Structural Equation Modeling, 23, 507–531.

    Article  Google Scholar 

  • Diallo, T. M. O, Morin, A. J. S., Lu, H. (2016b). The impact of total and partial inclusion or exclusion of active and inactive time invariant covariates in growth mixture models. Psychological Methods. doi:10.1037/met0000084.

  • Eccles, J. S. (1999). The development of children ages 6 to 14. The Future of Children: When School is Out, 9, 30–44. doi:10.2307/1602703.

    Article  Google Scholar 

  • Eccles, J. S. (2009). Who am I and what am I going to do with my life? Personal and collective identities as motivators of action. Educational Psychologist, 44, 78–89.

    Article  Google Scholar 

  • Eccles, J. S., Lord, S., & Midgley, C. (1991). What are we doing to early adolescents? The impact of educational contexts on early adolescents. American Journal of Education, 99, 521–542. doi:10.1086/443996.

    Article  Google Scholar 

  • Eccles, J. S., Wigfield, A., & Blumenfeld, P. C. (1984). Psychological predictors of competence development. Bethesda, MD: National Institute of Child Health and Human Development. (Grant NO. 2 R01 HD17553-01).

    Google Scholar 

  • Eccles, J. S., Wigfield, A., Harold, R. D., & Blumenfeld, P. (1993). Age and gender differences in children’s self- and task perceptions during elementary school. Child Development, 64, 830–847. doi:10.1111/j.1467-8624.1993.tb02946.x.

    Article  PubMed  Google Scholar 

  • Eccles, J. S., Wigfield, A., & Schiefele, U. (1998). Motivation to succeed. In N. Eisenberg (Ed.), Handbook of child psychology, vol. 3 of social, emotional, and personality development (pp. 1017–1095). Hoboken, NJ: Wiley.

    Google Scholar 

  • Farenga, S. J., & Joyce, B. A. (1999). Intentions of young students to enroll in science courses in the future: An examination of gender differences. Science Education, 83, 55–75.

    Article  Google Scholar 

  • Ferry, T. R., Fouad, N. A., & Smith, P. L. (2000). The role of family context in a social cognitive model for career-related choice behavior: A math and science perspective. Journal of Vocational Behavior, 57, 348–364. doi:10.1006/jvbe.1999.1743.

    Article  Google Scholar 

  • Fredricks, J. A., & Eccles, J. S. (2002). Children’s competence and value beliefs from childhood through adolsecence: Growth trajectories in two male-sex-typed domains. Developmental Psychology, 38, 519–533. doi:10.1037//0012-1649.38.4.519.

    Article  PubMed  Google Scholar 

  • Gottfried, A. E., Flemisng, J. S., & Gottfried, A. W. (2001). Continuity of academic intrinsic motivation from childhood through late adolescence: A longitudinal study. Journal of Educational Psychology, 93, 3–13. doi:10.1037/0022-0663.93.1.3.

    Article  Google Scholar 

  • Gottfried, A. E., Marcoulides, G. A., Gottfried, A. W., Oliver, P. H., & Guerin, D. W. (2007). Multivariate latent change modeling of developmental decline in academic intrinsic motivation and achievement: Childhood through adolescence. International Journal of Behavioral Development, 31, 317–327. doi:10.1177/0165025407077752.

    Article  Google Scholar 

  • Gottfried, A. E., Marcoulides, G. A., Gottfried, A. W., & Oliver, P. H. (2009). A latent curve model of parental motivational practices and developmental decline in math and science academic intrinsic motivation. Journal of Educational Psychology, 101, 729–739.

    Article  Google Scholar 

  • Hanson, S. L. (2007). Success in science among young African American women: The role of minority families. Journal of Family Issues, 28, 3–33. doi:10.1177/0192513X06292694.

    Article  Google Scholar 

  • Havard, N. (1996). Student attitudes to studying A-level sciences. Public Understanding of Science, 5, 321–330.

    Article  Google Scholar 

  • Huber, S. A., Häusler, J., Jurik, V., & Seidel, T. (2015). Self-underestimating students in physics instruction: Development over a school year and its connection to internal learning processes. Learning and Individual Differences, 43, 83–91.

    Article  Google Scholar 

  • Jacobs, J. E., Lanza, S., Osgood, D. W., Eccles, J. S., & Wigfield, A. (2002). Changes in children’s self-competence and values: Gender and domain differences across grades one through twelve. Child Development, 73, 509–527. doi:10.1111/1467-8624.00421.

    Article  PubMed  Google Scholar 

  • Jodl, K. M., Michael, A., Malanchuk, O., Eccles, J. S., & Sameroff, A. (2001). Parents’ roles in shaping early adolescents’ occupational aspirations. Child Development, 72, 1247–1265.

    Article  PubMed  Google Scholar 

  • Larose, S., Ratelle, C. F., Guay, F., Senécal, C., & Harvey, M. (2006). Trajectories of science self-efficacy beliefs during the college transition and academic and vocational adjustment in science and technology programs. Educational Research and Evaluation, 12, 373–393.

    Article  Google Scholar 

  • Lau, S., & Roeser, R. W. (2002). Cognitive abilities and motivational processes in high school students’ situational engagement and achievement in science. Educational Assessment, 8, 139–162. doi:10.1207/S15326977EA0802_04.

    Article  Google Scholar 

  • Li, Y., & Lerner, R. M. (2011). Trajectories of school engagement during adolescence: Implications for grades, depression, delinquency, and substance use. Developmental Psychology, 47, 233–247. doi:10.1037/a0021307.

    Article  PubMed  Google Scholar 

  • Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83, 1198–1202.

    Article  Google Scholar 

  • Lo, Y., Mendell, N., & Rubin, D. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767–778. doi:10.1093/biomet/88.3.767.

    Article  Google Scholar 

  • Lyons, T. (2006). The puzzle of falling enrolments in physics and chemistry courses: Putting some pieces together. Research in science education, 36, 285–311.

    Article  Google Scholar 

  • Maltese, A. V., Melki, C. S., & Wiebke, H. L. (2014). The nature of experiences responsible for the generation and maintenance of interest in STEM. Science Education, 98, 937–962.

    Article  Google Scholar 

  • Maltese, A. V., & Tai, R. H. (2010). Eyeballs in the fridge: Sources of early interest in science. International Journal of Science Education, 32, 669–685. doi:10.1080/09500690902792385.

    Article  Google Scholar 

  • Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among US students. Science Education, 95, 877–907. doi:10.1002/sce.20441.

    Article  Google Scholar 

  • Marsh, H. W., Hau, K. T., & Grayson, D. (2005). Goodness of fit evaluation in structural equation modeling. In A. Maydeu-Olivares, & J. McArdle (Eds.), Contemporary psychometrics. A festschrift for Roderick P. McDonald (pp. 275–340). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Marsh, H. W., Lüdtke, O., Trautwein, U., & Morin, A. J. S. (2009). Classical latent profile analysis of academic self-concept dimensions: synergy of person- and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling, 16, 191–225. doi:10.1080/10705510902751010.

    Article  Google Scholar 

  • Marsh, H. W., & Yeung, A. S. (1997). Causal effects of academic self-concept on academic achievement: Structural equation models of longitudinal data. Journal of Educational Psychology, 89, 41–54. doi:10.1037/0022-0663.89.1.41.

    Article  Google Scholar 

  • McLachlan, G., & Peel, D. (2000). Finite mixture models. New York, NY: Wiley.

    Book  Google Scholar 

  • Muthén, B. (2000). Latent variable mixture modeling. In G. A. Marcoulides, & R. E. Schumacker (Eds.), Advanced structural equation modeling: New developments and techniques (pp. 1–33). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Muthén, B. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. In L. M. Collins, & A. Sayer (Eds.), New methods for the analysis of change (pp. 291–322). Washington, DC: American Psychological Association.

    Chapter  Google Scholar 

  • Muthén, B. (2004). Latent variables analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 345–368). Newbury Park, CA: Sage.

    Google Scholar 

  • Muthén, L., & Muthén, B. (2015). Mplus (Version 7.31). Los Angeles, CA: Muthén & Muthén.

    Google Scholar 

  • Nagengast, B., & Marsh, H. W. (2012). Big fish in little ponds aspire more: Mediation and cross-cultural generalizability of school-average ability effects on self-concept and career aspirations in science. Journal of Educational Psychology, 104, 1033–1053.

    Article  Google Scholar 

  • Neathery, M. F. (1997). Elementary and secondary students’ perceptions toward science: Correlations with gender, ethnicity, ability, grade, and science achievement. Electronic Journal of Science Education, 2, 3–11.

    Google Scholar 

  • Nylund, K., Asparouhov, T., & Muthén, B. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling. A Monte Carlo simulation study. Structural Equation Modeling, 14, 535–569. doi:10.1080/10705510701575396.

    Article  Google Scholar 

  • Osborne, J. F., & Collins, S. (2000). Pupils’ and parents’ views of the school science curriculum. London: King’s College London.

    Google Scholar 

  • Osborne, J., Simon, S., & Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International Journal of Science Education, 25, 1049–1079.

    Article  Google Scholar 

  • Pajares, F. (2005). Gender differences in mathemtaics self-efficacy beliefs. In A. M. Gallagher, & J. C. Kaufman (Eds.), Gender differences in mathematics: An integrative psychological approach (pp. 294–315). New York, NY: Cambridge University Press.

    Google Scholar 

  • Phillips, D. A., & Zimmerman, M. (1990). The developmetnal course of perceived competence and incompetence among competent children. In R. J. Sternberg, & J. Kolligian (Eds.), Competence considered (pp. 41–66). New Haven, CT: Yale University Press.

    Google Scholar 

  • Ratelle, C. F., Guay, F., Larose, S., & Senécal, C. (2004). Family correlates of trajectories of academic motivation during a school transition: A semiparametric group-based approach. Journal of Educational Psychology, 96, 743–754. doi:10.1037/0022-0663.96.4.743.

    Article  Google Scholar 

  • Sadler, P. M., Sonnert, G., Hazari, Z., & Tai, R. (2012). Stability and volatility of STEM career interest in high school: A gender study. Science Education, 96, 411–427.

    Article  Google Scholar 

  • Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.

    Article  Google Scholar 

  • Simpkins, S. D., Davis-Kean, P. E., & Eccles, J. S. (2005). Parents’ socializing behaivor and children’s participation in math, science, and computer out-of-school activities. Applied Developmental Science, 9, 14–30. doi:10.1207/s1532480xads0901_3.

    Article  Google Scholar 

  • Simpkins, S. D., Davis-Kean, P. E., & Eccles, J. S. (2006). Math and science motivation: A longitudinal examination of the links between choices and beliefs. Developmental Psychology., 42, 70–83. doi:10.1037/0012-1649.42.1.70.

    Article  PubMed  Google Scholar 

  • Singh, K., Granville, M., & Dika, S. (2002). Mathematics and science achievement: Effects of motivation, interest, and academic engagement. The Journal of Educational Research, 95, 323–332. doi:10.1080/00220670209596607.

    Article  Google Scholar 

  • Stipek, D., & Mac Iver, D. (1989). Developmental change in children’s assessment of intellectual competence. Child Development, 60, 521–538. doi:10.2307/1130719.

    Article  Google Scholar 

  • Wang, M.-T. (2012). Educational and career interests in math: A longitudinal examination of the links between classroom environment, motivational beliefs, and interests. Developmental Psychology, 48, 1643–1657. doi:10.1037/a0027247.

    Article  PubMed  Google Scholar 

  • Wang, M.-T., & Degol, J. (2014). Staying engaged: Knowledge and research needs in student engagement. Child Development Perspectives, 8, 137–143. doi:10.1111/cdep.12073.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang, M.-T., & Degol, J. (2016). Gender gap in STEM: Current knowledge, implications for practice, policy, and future directions. Educational Psychology Review, 28, 1–22. doi:10.1007/s10648-015-9355-x.

    Article  Google Scholar 

  • Wang, M.-T., Degol, J. L., & Ye, F. (2015). Math achievement is important, but task values are critical too: Examining the intellectual and motivational factors leading to gender disparities in STEM careers. Frontiers in Psychology, 6, 1–9. doi:10.3389/fpsyg.2015.00036.

    Google Scholar 

  • Wang, M.-T., Fredricks, J., Ye, F., Hofkens, T., & Schall, J. (2016). The math and science engagement scale: Development, validation, and psychometric properties. Learning and Instruction, 43, 16–26.

    Article  Google Scholar 

  • Wang, M.-T., & Peck, S. C. (2013). Adolescent educational success and mental health vary across school engagement profiles. Developmental Psychology, 49, 1266–1276. doi:10.1037/a0030028.

    Article  PubMed  Google Scholar 

  • Wigfield, A., & Eccles, J. S. (1994). Children’s competence beliefs, achievement values, and general self-esteem change across elementary and middle school. The Journal of Early Adolescence, 14, 107–138. doi:10.1177/027243169401400203.

    Article  Google Scholar 

  • Wigfield, A., & Eccles, J. S. (2002). The development of competence beliefs, expectancies for success, and achievement values from childhood through adolescence. In A. Wigfield, & J. S. Eccles (Eds.), Development of achievement motivation (pp. 91–120). San Diego, CA: Academic Press.

    Chapter  Google Scholar 

  • Wigfield, A., Eccles, J. S., Schiefele, U., Roeser, R., Davis-Kean, P. (2006). Development of achievement motivation. In W. Damon, & R. M. Lerner (Series Eds.) & N. Eisenberg (Vol. Ed.) Handbook of child psychology, vol. 3. Social, emotional, and personality development (6th edn., pp. 933–1002). Hoboken, NJ: Wiley.

  • Wigfield, A., et al. (1997). Change in children’s competence beliefs and subjective task values across the elementary school years: A three-year study. Journal of Educational Psychology, 89, 451–469. doi:10.1037/0022-0663.89.3.451.

    Article  Google Scholar 

  • Wilkins, J. L. (2004). Mathematics and science self-concept: An international investigation. The Journal of Experimental Education, 72, 331–346. doi:10.3200/JEXE.72.4.331-346.

    Article  Google Scholar 

Download references

Funding

This project was supported by Grant DRL1315943 from the National Science Foundation and Grant HD074731-01 from the Eunice Kennedy Shriver National Institute of Child Health and Development (NICHD).

Authors’ Contributions

MTW conceived of the study, participated in its design and coordination and drafted the manuscript; AC participated in the design and interpretation of the data and performed the statistical analysis; JLD participated in the interpretation of the data and drafted the introduction and discussion sections of the manuscript; JSE participated in the design. MTW, AC, and JLD made equal intellectual contribution to the manuscript. All authors read and approved the final manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ming-Te Wang.

Ethics declarations

Conflict of Interest

The authors declare that they have no competing interests.

Ethical Approval

A review conducted by the Institutional Review Board approved the study to be consistent with the protection of the rights and welfare of human subjects and to meet the requirements of the Federal Guidelines. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Ming-Te Wang, Angela Chow, and Jessica Lauren Degol made equal intellectual contribution to the manuscript.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, MT., Chow, A., Degol, J.L. et al. Does Everyone’s Motivational Beliefs about Physical Science Decline in Secondary School?: Heterogeneity of Adolescents’ Achievement Motivation Trajectories in Physics and Chemistry. J Youth Adolescence 46, 1821–1838 (2017). https://doi.org/10.1007/s10964-016-0620-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10964-016-0620-1

Keywords

Navigation